Recent studies have indicated that human genetic variation has a "haplotype block structure" such that each chromosome can be decomposed into large blocks with strong linkage disequilibrium (LD) and relatively few haplotypes, separated by short regions of extensive recombination. The primary objective of this application is to study the biological significance of the observed haplotype structure and the practical implications of such haplotype structure for the mapping of genes responsible for human disease. To achieve this objective, we take an inter-disciplinary approach involving molecular biologists, population geneticists, genetic epidemiologists, statisticians, computer scientists, and mathematicians. We achieve the objective through five inter-related specific aims: (1) Develop efficient algorithms to improve the accuracy of polymorphism detection by both DNA sequencing and hybridization chips; (2) Develop efficient computational algorithms for haplotype block partitions and tag SNP selection; (3) Investigate the correspondence between the observed blocks and experimentally determined (primarily through genotyping of single sperm) rates of recombination; (4) Explore population genetics models of haplotype evolution that include alternative reasons for the presence of haplotype structure; and (5) Study the implications of haplotype block structure for association studies of both quantitative and qualitative traits, and develop novel statistical methods for association studies based on haplotype structure. We will validate and apply the newly developed methods on data from a variety of sources, both public and private. In addition to the scientific aims, we will train scientists in interdisciplinary, quantitative approaches to analyzing genomic polymorphism data, and in bioinformatics more generally. The need for such training is clear. We have a strong record of training in computational biology, and believe that we can provide a unique learning environment.